
import os

import numpy
import onnx






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       a.name
	lowercase	    =       b.name

	lowercase	    =       ''
	lowercase	    =       ''

	lowercase	    =       a == b

	lowercase	    =       name_a
	lowercase	    =       name_b

	return res






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	for i, input_name in enumerate(node_proto.input				):
		if input_name == name:
			node_proto.input.insert(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
			node_proto.input.pop(i + 1				)

	if node_proto.op_type == "If":
		_graph_replace_input_with(node_proto.attribute[0].g					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
		_graph_replace_input_with(node_proto.attribute[1].g					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	if node_proto.op_type == "Loop":
		_graph_replace_input_with(node_proto.attribute[0].g					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	for n in graph_proto.node:
		_node_replace_input_with(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       list(model.graph.initializer				)
	lowercase	    =       list(model_without_ext.graph.initializer				)
	for i, ref_i in ind_to_replace:
		assert inits_with_data[i].name == inits[i].name
		assert inits_with_data[ref_i].name == inits[ref_i].name
		assert i > ref_i

		lowercase	    =       inits[i].name
		lowercase	    =       inits[ref_i].name

		model_without_ext.graph.initializer.remove(inits[i]				)

		# for n in model.graph.node:
		_graph_replace_input_with(model_without_ext.graph					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       os.path.dirname(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       os.path.basename(__SCREAMING_SNAKE_CASE				)

	lowercase	    =       onnx.load(os.path.join(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)				)

	lowercase	    =       list(model.graph.initializer				)

	lowercase	    =       set()
	lowercase	    =       {}
	lowercase	    =       []

	lowercase	    =       0

	for i in range(len(__SCREAMING_SNAKE_CASE				)				):
		if i in dup_set:
			continue

		for j in range(i + 1					,				len(__SCREAMING_SNAKE_CASE				)				):
			if j in dup_set:
				continue
			if _is_equal_tensor_proto(inits[i]					,				inits[j]				):
				dup_set.add(__SCREAMING_SNAKE_CASE				)
				dup_set.add(__SCREAMING_SNAKE_CASE				)

				lowercase	    =       inits[j].data_type
				lowercase	    =       numpy.prod(inits[j].dims				)
				if dtype == 1:
					mem_size *= 4
				elif dtype == 6:
					mem_size *= 4
				elif dtype == 7 or dtype == 11:
					mem_size *= 8
				else:
					print('unexpected data type: '					,				__SCREAMING_SNAKE_CASE				)
				total_reduced_size += mem_size

				lowercase	    =       inits[i].name
				lowercase	    =       inits[j].name

				if name_i in dup_map:
					dup_map[name_i].append(__SCREAMING_SNAKE_CASE				)
				else:
					lowercase	    =       [name_j]
				ind_to_replace.append((j, i)				)

	print('total reduced size: '					,				total_reduced_size / 1024 / 1024 / 1024					,				'GB'				)

	lowercase	    =       sorted(__SCREAMING_SNAKE_CASE				)
	_remove_dup_initializers_from_model(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

	lowercase	    =       'optimized_' + model_file_name
	lowercase	    =       os.path.join(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	onnx.save(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

	return new_model



import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)